Atmospheric new particle formation identifier using longitudinal global particle number size distribution data

Simonas Kecorius; Leizel Madueño; Mario Lovric; Nikolina Racic; Maximilian Schwarz; Josef Cyrys; Juan Andrés Casquero-Vera; Lucas Alados-Arboledas; Sébastien Conil; Jean Sciare; Jakub Ondracek; Anna Gannet Hallar; Francisco J. Gómez-Moreno; Raymond Ellul; Adam Kristensson; Mar Sorribas; Nikolaos Kalivitis; Nikolaos Mihalopoulos; Annette Peters; Maria Gini; Konstantinos Eleftheriadis; Stergios Vratolis; Kim Jeongeun; Wolfram Birmili; Benjamin Bergmans; Nina Nikolova; Adelaide Dinoi; Daniele Contini; Angela Marinoni; Andres Alastuey; Tuukka Petäjä; Sergio Rodriguez; David Picard; Benjamin Brem; Max Priestman; David C. Green; David C.S. Beddows; Roy M. Harrison; Colin O’Dowd; Darius Ceburnis; Antti Hyvärinen; Bas Henzing; Suzanne Crumeyrolle; Jean Philippe Putaud; Paolo Laj; Kay Weinhold; Kristina Plauškaitė; Steigvilė Byčenkienė

doi:10.1038/s41597-024-04079-1

Atmospheric new particle formation identifier using longitudinal global particle number size distribution data

Simonas Kecorius, Leizel Madueño, Mario Lovric, Nikolina Racic, Maximilian Schwarz, Josef Cyrys, Juan Andrés Casquero-Vera, Lucas Alados-Arboledas, Sébastien Conil, Jean Sciare, Jakub Ondracek, Anna Gannet Hallar, Francisco J. Gómez-Moreno, Raymond Ellul, Adam Kristensson, Mar Sorribas, Nikolaos Kalivitis, Nikolaos Mihalopoulos, Annette Peters, Maria GiniKonstantinos Eleftheriadis, Stergios Vratolis, Kim Jeongeun, Wolfram Birmili, Benjamin Bergmans, Nina Nikolova, Adelaide Dinoi, Daniele Contini, Angela Marinoni, Andres Alastuey, Tuukka Petäjä, Sergio Rodriguez, David Picard, Benjamin Brem, Max Priestman, David C. Green, David C.S. Beddows, Roy M. Harrison, Colin O’Dowd, Darius Ceburnis, Antti Hyvärinen, Bas Henzing, Suzanne Crumeyrolle, Jean Philippe Putaud, Paolo Laj, Kay Weinhold, Kristina Plauškaitė, Steigvilė Byčenkienė

Physics

Research output: Contribution to a Journal (Peer & Non Peer) › Article › peer-review

1 Citation (Scopus)

Abstract

Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. The discussed data set can be used in future studies related to regional NPF.

Original language	English
Article number	1239
Journal	Scientific data
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41597-024-04079-1
Publication status	Published - Dec 2024

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Kecorius, S., Madueño, L., Lovric, M., Racic, N., Schwarz, M., Cyrys, J., Casquero-Vera, J. A., Alados-Arboledas, L., Conil, S., Sciare, J., Ondracek, J., Hallar, A. G., Gómez-Moreno, F. J., Ellul, R., Kristensson, A., Sorribas, M., Kalivitis, N., Mihalopoulos, N., Peters, A., ... Byčenkienė, S. (2024). Atmospheric new particle formation identifier using longitudinal global particle number size distribution data. Scientific data, 11(1), Article 1239. https://doi.org/10.1038/s41597-024-04079-1

@article{08b191e9d8ea4b5b83be2aa7bc30bce4,

title = "Atmospheric new particle formation identifier using longitudinal global particle number size distribution data",

abstract = "Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. The discussed data set can be used in future studies related to regional NPF.",

author = "Simonas Kecorius and Leizel Madue{\~n}o and Mario Lovric and Nikolina Racic and Maximilian Schwarz and Josef Cyrys and Casquero-Vera, {Juan Andr{\'e}s} and Lucas Alados-Arboledas and S{\'e}bastien Conil and Jean Sciare and Jakub Ondracek and Hallar, {Anna Gannet} and G{\'o}mez-Moreno, {Francisco J.} and Raymond Ellul and Adam Kristensson and Mar Sorribas and Nikolaos Kalivitis and Nikolaos Mihalopoulos and Annette Peters and Maria Gini and Konstantinos Eleftheriadis and Stergios Vratolis and Kim Jeongeun and Wolfram Birmili and Benjamin Bergmans and Nina Nikolova and Adelaide Dinoi and Daniele Contini and Angela Marinoni and Andres Alastuey and Tuukka Pet{\"a}j{\"a} and Sergio Rodriguez and David Picard and Benjamin Brem and Max Priestman and Green, {David C.} and Beddows, {David C.S.} and Harrison, {Roy M.} and Colin O{\textquoteright}Dowd and Darius Ceburnis and Antti Hyv{\"a}rinen and Bas Henzing and Suzanne Crumeyrolle and Putaud, {Jean Philippe} and Paolo Laj and Kay Weinhold and Kristina Plau{\v s}kaitė and Steigvilė By{\v c}enkienė",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",

year = "2024",

month = dec,

doi = "10.1038/s41597-024-04079-1",

language = "English",

volume = "11",

journal = "Scientific data",

issn = "2052-4463",

number = "1",

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Kecorius, S, Madueño, L, Lovric, M, Racic, N, Schwarz, M, Cyrys, J, Casquero-Vera, JA, Alados-Arboledas, L, Conil, S, Sciare, J, Ondracek, J, Hallar, AG, Gómez-Moreno, FJ, Ellul, R, Kristensson, A, Sorribas, M, Kalivitis, N, Mihalopoulos, N, Peters, A, Gini, M, Eleftheriadis, K, Vratolis, S, Jeongeun, K, Birmili, W, Bergmans, B, Nikolova, N, Dinoi, A, Contini, D, Marinoni, A, Alastuey, A, Petäjä, T, Rodriguez, S, Picard, D, Brem, B, Priestman, M, Green, DC, Beddows, DCS, Harrison, RM, O’Dowd, C, Ceburnis, D, Hyvärinen, A, Henzing, B, Crumeyrolle, S, Putaud, JP, Laj, P, Weinhold, K, Plauškaitė, K & Byčenkienė, S 2024, 'Atmospheric new particle formation identifier using longitudinal global particle number size distribution data', Scientific data, vol. 11, no. 1, 1239. https://doi.org/10.1038/s41597-024-04079-1

TY - JOUR

T1 - Atmospheric new particle formation identifier using longitudinal global particle number size distribution data

AU - Kecorius, Simonas

AU - Madueño, Leizel

AU - Lovric, Mario

AU - Racic, Nikolina

AU - Schwarz, Maximilian

AU - Cyrys, Josef

AU - Casquero-Vera, Juan Andrés

AU - Alados-Arboledas, Lucas

AU - Conil, Sébastien

AU - Sciare, Jean

AU - Ondracek, Jakub

AU - Hallar, Anna Gannet

AU - Gómez-Moreno, Francisco J.

AU - Ellul, Raymond

AU - Kristensson, Adam

AU - Sorribas, Mar

AU - Kalivitis, Nikolaos

AU - Mihalopoulos, Nikolaos

AU - Peters, Annette

AU - Gini, Maria

AU - Eleftheriadis, Konstantinos

AU - Vratolis, Stergios

AU - Jeongeun, Kim

AU - Birmili, Wolfram

AU - Bergmans, Benjamin

AU - Nikolova, Nina

AU - Dinoi, Adelaide

AU - Contini, Daniele

AU - Marinoni, Angela

AU - Alastuey, Andres

AU - Petäjä, Tuukka

AU - Rodriguez, Sergio

AU - Picard, David

AU - Brem, Benjamin

AU - Priestman, Max

AU - Green, David C.

AU - Beddows, David C.S.

AU - Harrison, Roy M.

AU - O’Dowd, Colin

AU - Ceburnis, Darius

AU - Hyvärinen, Antti

AU - Henzing, Bas

AU - Crumeyrolle, Suzanne

AU - Putaud, Jean Philippe

AU - Laj, Paolo

AU - Weinhold, Kay

AU - Plauškaitė, Kristina

AU - Byčenkienė, Steigvilė

PY - 2024/12

Y1 - 2024/12

N2 - Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. The discussed data set can be used in future studies related to regional NPF.

AB - Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. The discussed data set can be used in future studies related to regional NPF.

UR - http://www.scopus.com/inward/record.url?scp=85209196638&partnerID=8YFLogxK

U2 - 10.1038/s41597-024-04079-1

DO - 10.1038/s41597-024-04079-1

M3 - Article

C2 - 39550387

AN - SCOPUS:85209196638

SN - 2052-4463

VL - 11

JO - Scientific data

JF - Scientific data

IS - 1

M1 - 1239

ER -

Atmospheric new particle formation identifier using longitudinal global particle number size distribution data

Abstract

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